Reproduction Operators Research Articles

A Tri-Objective Method for Bi-Objective Feature Selection in Classification.

Minimizing the number of selected features and maximizing the classification performance are two main objectives in feature selection, which can be formulated as a bi-objective optimization problem. Due to the complex interactions between features, a solution (i.e., feature subset) with poor objective values does not mean that all the features it selects are useless, as some of them combined with other complementary features can greatly improve the classification performance. Thus, it is necessary to consider not only the performance of feature subsets in the objective space, but also their differences in the search space, to explore more promising feature combinations. To this end, this paper proposes a tri-objective method for bi-objective feature selection in classification, which solves a bi-objective feature selection problem as a tri-objective problem by considering the diversity (differences) between feature subsets in the search space as the third objective. The selection based on the converted tri-objective method can maintain a balance between minimizing the number of selected features, maximizing the classification performance, and exploring more promising feature subsets. Furthermore, a novel initialization strategy and an offspring reproduction operator are proposed to promote the diversity of feature subsets in the objective space and improve the search ability, respectively. The proposed algorithm is compared with five multiobjective-based feature selection methods, six typical feature selection methods, and two peer methods with diversity as a helper objective. Experimental results on 20 real-world classification datasets suggest that the proposed method outperforms the compared methods in most scenarios.

A fixed point evolution algorithm based on expanded Aitken rapid iteration method for global numeric optimization

Evolution algorithms based on mathematical models whose reproduction operators are derived from mathematical models are a promising branch of metaheuristic algorithms. Aitken rapid iteration method, as a fixed point iteration technique for solving nonlinear equations, performs a procedure of progressive display of a root and generates an iterative sequence that exhibits a convergent trend. Inspired by the idea that an iterative sequence gradually converges to the optimal point during the progressive display procedure of a fixed point of an equation, a fixed point evolution algorithm based on the expanded Aitken rapid iteration method (FPEea) is proposed. To develop FPEea, an expanded Aitken rapid model is first constructed. Then, three polynomials which are derived from the expanded Aitken rapid model are used as the reproduction operator of FPEea to produce offspring. The performance of FPEea is investigated on CEC2019 and CEC2020 benchmark function sets, as well as four engineering design problems. Experimental results show that FPEea is an effective and competitive algorithm compared with several classical evolution algorithms and state-of-the-art algorithms.

Many-objective evolutionary algorithm with multi-strategy selection mechanism and adaptive reproduction operation

Many-objective evolutionary algorithm with multi-strategy selection mechanism and adaptive reproduction operation

Research orientation and novelty discriminant for new metaheuristic algorithms

The rapid rate of generating a new metaheuristic algorithm almost every month is causing increasing concerns and disputes about their novelty. To stop the disputes and steer algorithm design in a healthy direction, this article presents a discriminant method of novelty and a research orientation for metaheuristic algorithms. The novelty discriminant is implemented by two novel mathematical definitions of homologous algorithms and root algorithms. The two definitions are developed to divide algorithms into two classes according to a discrepancy in whether the reproduction operator of an algorithm is a linear combination of existing operators. Root algorithms are strongly innovative because of the novelty of their reproduction operators. A homologous algorithm is recognized as a novel algorithm only when the practical value and academic significance of the new combinatorial structure of the algorithm’s reproduction operator is clearly highlighted. So a research orientation that the study of a homologous algorithm should focus on how a certain metaphor evokes a new combinatorial structure can be developed. Moreover, numerical experiments should be conducted to analyze the relationship between its search behavior and its new combinatorial structure. Further work can be directed towards studying the systematization of existing knowledge about search behaviors of metaheuristic algorithms.

Population Feasibility State Guided Autonomous Constrained Multi-Objective Evolutionary Optimization

Many practical problems can be classified as constrained multi-objective optimization problems. Although various methods have been proposed for solving constrained multi-objective optimization problems, there is still a lack of research considering the integration of multiple constraint handling techniques. Given this, this paper combines the objective and constraint separation method with the multi-operator method, proposing a population feasibility state guided autonomous constrained evolutionary optimization method. This method first defines the feasibility state of the population based on both feasibility and ε feasibility of the solutions. Subsequently, a reinforcement learning model is employed to construct a mapping model between the population state and reproduction operators. Finally, based on the real-time population state, the mapping model is utilized to recommend the promising reproduction operator for the next generation. This approach demonstrates significant performance improvement for ε constrained mechanisms in constrained multi-objective optimization algorithms, and shows considerable advantages in comparison with state-of-the-art constrained multi-objective optimization algorithms.

A Grey Prediction‐Based Reproduction Strategy for Many‐Objective Evolutionary Algorithm

Many‐objective evolutionary algorithms (MaOEAs) consisted of environmental selection and reproduction operator. However, few studies focus on how to design reproduction operators to improve the performance of MaOEAs. In this paper, a reproduction operator based on grey prediction is proposed for MaOEAs, named GPRS. Specifically, the grey prediction assisted by reference vector is first used to get the target location. Then, a fine regulation is designed to generate potential solutions by handling the different information further. Finally, a gene sharing strategy is executed to accelerate the convergence by information exchange. The effectiveness of the proposed reproduction strategy is validated by comparing it with five widely used reproduction operators by embedding into a classical framework NSGAIII. At the same time, an improved NSGAIIIGPRS is developed by embedding the proposed GPRS and compared with seven excellent algorithms on a number of benchmark problems and one practical application. The final experimental results show that the proposed GPRS has significant advantages over similar reproduction strategies, and the improved NSGAIIGRPS is more effective compared with other excellent algorithms in handling many‐objective optimization problem.

An improved linear prediction evolution algorithm based on topological opposition-based learning for optimization

Prediction-based evolutionary algorithm is one of the emerging category of meta-heuristic optimization techniques. The improved linear prediction evolution algorithm (ILPE) is a recently developed meta-heuristic optimization technique that draws inspiration from non-linear least-square fitting models. This article implements the concept of topological opposition-based learning, which was first applied in grey prediction evolutionary algorithms to the ILPE. In traditional evolutionary algorithms, after employing the mutation and crossover operator, it generates trial populations. The proposed algorithm constructs a new reproduction operator using the non-linear least square fitting model with topological opposition-based learning to generate trial individuals. This reproduction operator considers the population series as a time series and uses the topological opposition-based non-linear least square fitting model to predict the next generation of populations. The efficiency and accuracy of the algorithm are demonstrated through numerical experiments on CEC2014 and CEC2017 benchmark functions. The results of these experiments show that the proposed algorithm is highly effective in solving optimization problems.•An improved linear prediction evolution algorithm based on topological opposition based learning (TILPE) is proposed.•The proposed strategy treat the the population series as a time series.•To validate the efficacy of TILPE, CEC2014 and CEC2017 benchmark functions are used.

Multi-objective optimization strategy for industrial catalytic cracking units: Kinetic model and enhanced SPEA-2 algorithm with economic, CO2, and SO2 emission considerations

The multi-objective optimization of the economics and environmental protection of the fluidized catalytic cracking reaction-regeneration (FCC-RR) process can achieve a balanced production of light olefins, and other chemicals while mitigating pollutant gas emissions. This paper proposes a novel multi-objective optimization framework that integrates the FCC-RR lumped kinetic model and SPEA-2 algorithm based on the path evolution reproduction operator. The proposed FCC-RR model not only provides insights into the influence of various reaction variables on product yields from a mechanistic perspective but also accurately simulates the distribution of products during the reaction-regeneration process. Additionally, we present an improved SPEA-2 algorithm that incorporates the path evolution operator and applies it to a real FCC-RR device to speed up the optimization process in terms of solution speed and quality. The results of the case study demonstrate that the proposed optimization framework has significant advantages in solving multi-objective optimization in the catalytic cracking process.

The Directed Multi-Objective Estimation Distribution Algorithm (D-MOEDA)

Improvement Direction Mapping (IDM) methods have been applied as a local search strategy to hybridize global search algorithms. A natural question is whether this concept could be applied within a global search scheme, so that the stochastic search operators are directed toward promising regions, promoting a more efficient search. This paper introduces a novel Multi-Objective Evolutionary Algorithm (MOEA) that incorporates the IDM into the reproduction operator of an Estimation of Distribution Algorithm (EDA). In this proposal, the search directions of the IDM based on aggregation functions are used to directly steer the search process of a multi-objective evolutionary algorithm based on decomposition, by orienting a local probability distribution towards a search direction, the proposal intends to steer solutions toward the Pareto front (PF) of the Multi-Objective Optimization Problem (MOP), exploiting the search features of the aggregation functions. The proposal is tested using a set of well-known benchmark MOPs and compared to state of the art MOEAs. Results showed statistical evidence about the importance of the orientation of the search probability distribution to improve the convergence to the Pareto front.

A Q-Learning Proposal for Tuning Genetic Algorithms in Flexible Job Shop Scheduling Problems

Genetic algorithms (GAs) belong to the category of evolutionary algorithms and are frequently utilized for resolving challenging combinatorial problems. However, they typically require customization to suit a particular problem type, and their performance is heavily influenced by numerous hyperparameters and reproduction operators. In this work, we propose a Reinforcement Learning approach for fine-tuning Genetic Algorithms in Flexible Job Shop Scheduling problems (FJSP), where the main parameters involved in the genetic algorithm operators are trained to allocate the most promising values. The approach returns an optimized schedule taking into account given constraints specific to the scenario, such as the relationship among release date, due date, and processing time, which machines must be selected out of a set of alternative machines, or which sequence-dependent setup time can be filtered.
 The approach takes input data in the form of FJSP instances by varying the numbers of jobs and machines and then uses the NSGA-II algorithm to generate solutions. These solutions are stored in a Solutions module and they are analyzed using a Principal components analysis (PCA) to identify clusters of similar instances and solutions. The Q-Learning module then generates hyperparameters for each iteration of the NSGA-II algorithm based on information from the previous modules. A toy example is presented to better understand the behavior of the proposal and the results obtained for optimizing further instances of the problem in a more efficient way.

Learning to Accelerate Evolutionary Search for Large-Scale Multiobjective Optimization

Most existing evolutionary search strategies are not so efficient when directly handling the decision space of large-scale multiobjective optimization problems (LMOPs). To enhance the efficiency of tackling LMOPs, this article proposes an accelerated evolutionary search (AES) strategy. Its main idea is to learn a gradient-descent-like direction vector (GDV) for each solution via the specially trained feedforward neural network, which may be the learnt possibly fastest convergent direction to reproduce new solutions efficiently. To be specific, a multilayer perceptron (MLP) with only one hidden layer is constructed, in which the number of neurons in the input and output layers is equal to the dimension of the decision space. Then, to get appropriate training data for the model, the current population is divided into two subsets based on the nondominated sorting, and each poor solution in one subset with worse convergence will be paired to an elitist solution in another subset with the minimum angle to it, which is considered most likely to guide it with rapid convergence. Next, this MLP is updated via backpropagation with gradient descent by using the above elaborately prepared dataset. Finally, an accelerated large-scale multiobjective evolutionary algorithm (ALMOEA) is designed by using AES as a reproduction operator. Experimental studies validate the effectiveness of the proposed AES when handling the search space of LMOPs with dimensionality ranging from 1000 to 10000. When compared with six state-of-the-art evolutionary algorithms, the experimental results also show the better efficiency and performance of the proposed optimizer in solving various LMOPs.

Ensemble-Learning-Based Multiobjective Optimization for Antenna Design

An ensemble-learning-based multiobjective optimization is proposed for antenna design. By integrating the local search into multiobjective evolutionary algorithm based on decomposition (MOEA/D) and selecting appropriate solutions of the local search acquired with different offspring reproduction (OR) operators, the MOEA/D combined with ensemble OR (MOEA/D-EOR) is presented. Parallel local OR operators based on samples are also merged, for the first time, by exhaustively mining the evolution data of the optimization searching. The diversity and convergence of MOEA/D-EOR are verified by several widely used benchmark problems. The efficiency of MOEA/D-EOR is demonstrated by designing a high-performance bow-tie multiple-input and multiple-output (MIMO) antenna, which saves at least 25% of the optimization time. The overall performance of MOEA/D-EOR is further demonstrated by designing a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> 2 MIMO patch antenna in a compact size of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.667\lambda _{0} \times 0.667\lambda _{0}$ </tex-math></inline-formula> , which achieves a high isolation of 24 dB in an operation band of 4.95–5.07 GHz.

Municipal food waste recycling as an environmental strategy: a game-theoretical approach.

With the expansion of urbanization, we are witnessing the growing uncertainty in municipal food demand leading to an increase in urban waste. With the motive of producing organic fertilizers and conserving the environment, expired food can be collected and recycled. This study examines the hypothesis that leasing recycling facilities from peri-urban areas, due to the ban on reproduction operations in the city centers, can manage the recycling system participants' relationship and enhance sustainability in urban communities. The problem has been investigated under two separate sources of uncertainty, namely, quality and capacity. In the first scenario, a recycling system consisting of a commercial food service located in urban areas, a food waste collection agency, and a suburban fertilizer factory is optimized, in which the commercial food service leases the fertilizer factory's facilities for recycling operations. In the second scenario, the two factories' relationship, in which the first factory can rent the second factory's facilities in case of capacity shortage, is managed through hybrid contracts and mathematical programming models. The results show that the whole system optimization and Pareto Improvement results for all members are guaranteed under proposed hybrid contracts. These conclusions can help food recycling system managers have a better relationship with other players in their supply chains and enhance their credibility for caring about the environment, social concerns, and government compliance.

Automated University Lecture Schedule Generator based on Evolutionary Algorithm

university is a complicated work so in the implementation it have violation of the constraints and it also takes a lot of time since it is created manually. In this paper evolutionary algorithm (EA) is used to create an effective and feasible schedules based on the real data input that is obtained from each department. The objective functions in EA contribute in gaining the fitness function to solve the constraints problem in the schedule by applying weighting for each hard constraints. The objective function is gained from the total of infringement in each soft constraints addition by score weighting. The genetic operator used in EA is stochastic variation Operator. As far as the reproduction operator is concerned, the tournament selection was used with size 3. Crossover operator is conducted after selection process with crossover probability equal to 0.05 and mutation rate is 0.1. The size of population was set to 9 and stopping criteria algorithm was left run for fitness value = 1. The simulation result shows that EA can create lecture schedules efficiently and feasibly. Moreover, it is also faster with the execution time of the proposed EA is less than 30 and easier than creating manually.

Dynamics of Dirac concentrations in the evolution of quantitative alleles with sexual reproduction

A proper understanding of the links between varying gene expression levels and complex trait adaptation is still lacking, despite recent advances in sequencing techniques leading to new insights on their importance in some evolutionary processes. This calls for extensions of the continuum-of-alleles framework first introduced by Kimura (1965 Proc. Natl Acad. Sci. USA 54 731–36) that bypass the classical Gaussian approximation. Here, we propose a novel mathematical framework to study the evolutionary dynamics of quantitative alleles for sexually reproducing populations under natural selection and competition through an integro-differential equation. It involves a new reproduction operator which is nonlinear and nonlocal. This reproduction operator is different from the infinitesimal operator used in other studies with sexual reproduction because of different underlying genetic structures. In an asymptotic regime where initially the population has a small phenotypic variance, we analyse the long-term dynamics of the phenotypic distributions according to the methodology of small variance (Diekmann et al 2005 Theor. Popul. Biol. 67 257–71). In particular, we prove that the reproduction operator strains the limit distribution to be a product measure. Under some assumptions on the limit equation, we show that the population remains monomorphic, that is the phenotypic distribution remains concentrated as a moving Dirac mass. Moreover, in the case of a monomorphic distribution, we derive a canonical equation describing the dynamics of the dominant alleles.

Multiobjective Optimization for Improving Throughput and Energy Efficiency in UAV-Enabled IoT

Unmanned-aerial-vehicle (UAV)-aided wireless communication in Internet of Things (IoT) applications is becoming the focus of attention of researchers. This article investigates a UAV-assisted communication system for serving IoT, in which multiple rotary-wing UAVs are employed to communicate with multiple terrestrial IoT devices. Specifically, we formulate a UAV deployment multiobjective optimization problem (UAVDMOP) to simultaneously maximize the minimum throughput of the UAV–device pairs, maximize the total throughput of the whole system, and minimize the total energy consumptions of UAVs via a joint optimization of the locations of UAVs, transmission power of UAVs, and association relationship between the UAVs and IoT devices. UAVDMOP consists of both discrete and continuous solution spaces, which is difficult to be solved. Thus, we propose an improved discrete and continuous multiobjective evolutionary algorithm based on decomposition (IDCMOEA/D) with a hybrid solution initialization operation and a hybrid solution reproduction operation for increasing the performance of the algorithm so that making it more suitable for dealing with the UAVDMOP. Simulation results demonstrate that the proposed method is effective to enhance the throughput and energy efficiency of the system and it has superior performance compared to other methods.

Multisource Heterogeneous User-Generated Contents-Driven Interactive Estimation of Distribution Algorithms for Personalized Search

Personalized search is essentially a complex qualitative optimization problem, and interactive evolutionary algorithms (EAs) have been extended from EAs to adapt to solving it. However, the multisource user-generated contents (UGCs) in the personalized services have not been concerned on in the adaptation. Accordingly, we here present an enhanced restricted Boltzmann machine (RBM)-driven interactive estimation of distribution algorithms (IEDAs) with multisource heterogeneous data from the viewpoint of effectively extracting users’ preferences and requirements from UGCs to strengthen the performance of IEDA for personalized search. The multisource heterogeneous UGCs, including users’ ratings and reviews, items’ category tags, social networks, and other available information, are sufficiently collected and represented to construct an RBM-based model to extract users’ comprehensive preferences. With this RBM, the probability model for conducting the reproduction operator of estimation of distribution algorithms (EDAs) and the surrogate for quantitatively evaluating an individual (item) fitness are further developed to enhance the EDA-based personalized search. The UGCs-driven IEDA is applied to various publicly released Amazon datasets, e.g., recommendation of Digital Music, Apps for Android, Movies, and TV, to experimentally demonstrate its performance in efficiently improving the IEDA in personalized search with less interactions and higher satisfaction.

Reproduction operators in solving LABS problem using EMAS meta-heuristic with various local optimization techniques

ABSTRACT Agent-based evolutionary, computational systems have been proven to be an efficient concept for solving complex computational problems. This paper is an extension of [Biełaszek, S., Piętak, K., & Kisiel-Dorohinicki, M. (2021). New extensions of reproduction operators in solving LABS problem using EMAS meta-heuristic. Springer, cop. 2021. – Lecture Notes in Artificial Intelligence, Computational collective intelligence 12876 304-316. 13th International Conference, ICCCI 2021: Rhodes, Greece, September 29ŰOctober 1, 2021.] where we proposed new variants of reproduction operators together with new heuristics for the generation of initial population, dedicated to LABS – a hard discrete optimization problem. In this research, we verify if the proposed recombination operators improve EMAS efficiency also with different local optimization techniques such as Tabu Search and Self-avoiding walk, and therefore can be seen as better recombination operators dedicated to LABS problem in general. This paper recalls the definition of new recombination variants dedicated to LABS and verify if they can be successfully used in many different evolutionary configurations.

Automatic Text Summarization for Hindi Using Real Coded Genetic Algorithm

In the present scenario, Automatic Text Summarization (ATS) is in great demand to address the ever-growing volume of text data available online to discover relevant information faster. In this research, the ATS methodology is proposed for the Hindi language using Real Coded Genetic Algorithm (RCGA) over the health corpus, available in the Kaggle dataset. The methodology comprises five phases: preprocessing, feature extraction, processing, sentence ranking, and summary generation. Rigorous experimentation on varied feature sets is performed where distinguishing features, namely- sentence similarity and named entity features are combined with others for computing the evaluation metrics. The top 14 feature combinations are evaluated through Recall-Oriented Understudy for Gisting Evaluation (ROUGE) measure. RCGA computes appropriate feature weights through strings of features, chromosomes selection, and reproduction operators: Simulating Binary Crossover and Polynomial Mutation. To extract the highest scored sentences as the corpus summary, different compression rates are tested. In comparison with existing summarization tools, the ATS extractive method gives a summary reduction of 65%.

An efficient cyber‐physical system using hybridized enhanced support‐vector machine with Ada‐Boost classification algorithm

SummaryThe necessity of cyber‐security has obtained immense importance in day‐to‐day concerns of network communication. Therefore, several available research works predominantly focus on network security to protect the resources, services, and networks from any unauthorized access. A CPS (cyber‐physical system) model using a dual mutation‐based genetic algorithm, with feature classification through Ada‐Boost and SVM classifier is proposed in this paper. Dual‐mutation based genetic‐algorithm overcomes the issues of conventional techniques including convergence issues and local fine‐tuning of features. In this paper, necessary modifications were made to the existing Genetic Algorithm (GA) method to reduce the random nature of the traditional GA method. Particularly, the goal of this work is to develop the modified reproduction operators with appropriate fitness functions to guide simulations to gain optimal solutions. In floating‐point representation, every chromosome vector has been coded as a floating‐point number vector having the same length as the solution vector. Each element was selected initially, to stand within the desired domain, and operators were designed carefully in satisfying the constraints. As a result, there are various enhancements employed in the dual‐mutation algorithm that handles local fine‐tuned features. The relevant features of dataset samples are extracted and rescaled using feature selection and resampling phase aided by the Markov‐resampling process. Followed by this, a hybrid approach of ESVM (enhanced support‐vector machine) algorithm with Ada‐Boost classifier is implemented for the fault classification process. The performance assessment was explicated in terms of accuracy‐factor, F1‐score, and execution time. Comparative analysis expounded the efficacy of the proposed model than other conventional methods attaining higher accuracy (97%), F1‐score (99%) rates, and less execution time (15.33 s).